The present invention relates to remote sensing. More particularly, the present invention relates to a method for classification of remote scenes by decorrelation statistical analysis of spectra systematically derived from the scenes. The invention further relates to hardware for such classification, the hardware is dedicated or tunable according to parameters derived from the decorrelation statistical analysis.
A thorough tutorial concerning the evolution, methods, strategies, devices and applications of remote sensing is given in J. N. Rinker "Remote Sensing Tutorial--Multiband, Multispectral and Hyper spectral" U.S. Army Topographic Engineering Center, Fort Belvoir, Va. 22060-5546, USA, which is incorporated by reference as if fully set forth herein. The following background reconstructs the highlights of this publication.
From the standpoint of image analysis, an image provides two sources of information. One is shape, and the other is intensity of tones and colors. The analysis of shape is a manual procedure and is still the state-of-the-art for getting terrain information about physical properties and conditions in relation to engineering applications such as site selection and evaluation, and to military applications such as cross-country movement. The second source of information is the analysis and evaluation of the colors, tones, and their intensities associated with a given pattern element. Although analysis of these characteristics can be done to some extent by a manual, or "eyeball" evaluation, it is here that digital analysis and computer techniques take over.
The need to get information by observation from afar is old. The techniques employed are very diverse, ranging from watching events from a hill, to probing the depths of space with the latest in technology. The workhorse for obtaining terrain information is electromagnetic radiation, be it reflected, emitted, or luminesced. Such radiation comes from natural sources, such as the sun, as well as from man-made sources. How it is measured is of equal diversity and includes sensors such as the eye, photographic emulsions, photo cells, antennae, charge coupled devices, thermistors, etc. Most often, the results from remote collection systems are presented as an image, or an assembly of images, wherein each image portrays the terrain in a different part of the electromagnetic spectrum.
Reliable, detailed information about the landscape in terms of composition, structure, properties, conditions, and use, are fundamental factors needed for predicting terrain characteristics for engineering site selection and evaluation locating engineering materials, environmental impact and response to stress, cross-country movement, and selecting potential ground water sites and subsurface waste disposal sites, to name but a few. The determination of these factors is based on the manual, or "eyeball", evaluation of shape patterns, especially three-dimensional or stereoscopic shapes, such as landform and drainage, augmented by an evaluation of the patterns of vegetation, cultural development, lineations, and tone and texture. Although information can be derived from monoscopic imagery, there are severe limitations to its quality and quantity. For obtaining this type of terrain information, the manual analysis of stereo imagery is still state-of-the-art (Rinker, J. N., and P. A, Corl 1984: Air photo analysis, photo interpretation logic, and feature extraction. U.S. Army Engineer Topographic Laboratories Report, ETL-0329, Fort Belvior, Va. 22060-5546; Rinker, J. N., and P. A, Corl 1989: An analysis of air photo and radar imagery of Barro Colorado Island, Paname. U.S. Army Engineer Topographic Laboratories Report, ETL-0540, Fort Belvior, Va. 22060-5546;). Digital analysis contributes little. Digital techniques such as band combinations, enhancement, etc., can, however, improve pattern boundaries for visual observation.
Targeting refers to the detection and, hopefully, the identification of specific features, items, or conditions. For success, the target must have some characteristic that differs from its background and which cannot be confused with any other feature in the field of view. For point targets, this is seldom the case. The prediction of detectability is based on physics, and more often than not, this can be dealt with. But, more often than not, identification is iffy, mostly because of signals that, in the spectral band of use, look like the target. For passive systems such as thermal infrared and thermal microwave, these issues are more complex than for the active systems. In general, the more cluttered the scene, for any type of sensor, the more difficult the identification of point source targets. Detection based on shape, size, and arrangement, includes examples such as roads, airports, dams, vehicles, crop/field patterns, structures, and urban areas, and such are usually easier to identify by manual analysis. In addition to shape, differences can also be based in color, spectral reflectance, spectral emittance (temperature), luminescence, acoustical reflectance and emittance, magnetic fields, etc. Application examples include road type (asphalt or cement concrete), diseased vegetation, stressed vegetation, flood boundaries, wetland areas, thermal springs, thermal plumes for power plants, oil slicks, hot spots in burned areas, alteration zones, number of lakes in a region, camouflaged sites, and military units and equipment. Included in targeting are the applications of change detection and monitoring, e.g., seasonal changes in wetlands, desertification, alteration of land use, forest clearing, extent of pack ice etc. Although aerial photography and multiband images are used in these tasks, it is here that computer assisted techniques and digital analysis become important.
For targeting, military or civil, the basic premise is that the more bands one uses to examine the scene, the chances for detection and identification increase. A camouflaged target may resemble the terrain visually, photographically, and thermally, but show a mismatch in the radar bands; or, match in the radio frequencies, but not in the thermal infrared; or match in radar and thermal infrared, but show a mismatch in its solar spectral signature.
Research and development for improving remote sensing capabilities concentrates on two aspects-data analysis and data collection. With reference to data analysis, the field has gone from dependence on manual analysis, or "eyeball" evaluation of image patterns, to the interweaving of spectral data and computer techniques, and, in some cases to the extent of automated procedures. With reference to data collection, the trend has been towards more, as well as narrower spectral bands. The rationale is support of this evolution is the fact that the finer the slice of spectral data, and the more bands that are used, the greater the ability to establish identities and conditions.
In the 1860s, James Clerk Maxwell developed his famous set of four relatively simple equations that established the relations between electricity and magnetism under all conditions, and which combined electricity, magnetism, and light into a single concept. These equations showed that it was no longer possible to consider an electric field and a magnetic field as separate and isolated components. These two fields will always be present together, at mutual right angles-coalesced into an entity known as electromagnetic field, with the electric field associated with voltage, and the magnetic filed associated with current. A changing electric filed induces changes in the magnetic field, which, in turn, induces changes in the electric field, which in turn induces changes in the magnetic field. and on and on as the field propagates outward in all directions-playing leapfrog to infinity. This alternation, which starts at the leading edge of the pulse. can take place only so fast, no more and no less a defining rate at which each field can produce the other. It is this that establishes the speed of travel, and why it is a constant--a constant known as the speed of light in empty space, i.e., a vacuum. A feature of Maxwell's equations, which astounded physicists at the time, was the fact that the speed of propagation predicted by the equations for an electromagnetic field was same value as the speed of light, which had already been measured.
This spread of frequencies is known as the electromagnetic (EM) spectrum, and locations within it can be specified by numbers for frequency (cycles per second) and wavelength (Angstroms, nanometers, micra, feet, miles, etc.), or by words. Words that designate frequency locations are radio terms such as Low Frequency (LF), High Frequency (HF), Very High Frequency (VHF), Ultra High Frequency (UHF), and Extraordinary High Frequency (EHF), at which point they ran out of superlatives. On the wavelength side, amateur radio operators use the terms, 1 meter band, 10 meter band, etc. General descriptors for various portions of the EM spectrum, going from high frequency to low, or from short wavelengths to long, include: gamma ray, x-ray, ultraviolet, visible, infrared, microwave, and radio. The visible part of the EM spectrum covers the approximate wavelength range of 400-700 nanometers (nm). It is not a sharp cutotT at the long wavelength end, and many people can perceive photons with wavelengths well beyond 700 nm. The 400-500 nm band is called blue, the 500-600 nm band green, and the 600-700 nm band red. Wavelengths shorter than 400 nm fall in the ultraviolet region, and wavelengths longer than 700 nm are in the infrared domain, which extends out to a wavelength of 1 mm. Beyond this is the microwave region.
When electromagnetic energy falls on a material, be it gas, liquid, or solid, several things can happen. What happens depends on the electrical properties of the materials, i.e., the index of refraction, or dielectric constant. In turn, this is a function of the oscillation frequency of the incoming electric field. More exactly, what happens depends on changes in the electrical properties between the medium the radiation it is in, and the medium it is entering. If, in going from one medium to another, the radiation encounters a change in electrical properties that takes place in a distance less than its characteristic wavelength, then something must happen to that radiation--it cannot continue as it was. It will undergo, singly or in combination, reflection (scattering), refraction, or absorption.
Because all materials reflect, absorb, or emit photons in ways characteristic of their molecular makeup, a high resolution trace of the intensity of the transmitted, reflected, emitted, or luminesced radiation versus wavelength forms a graphical record unique to a given material. Different materials cannot have identical wave shapes of reflectance, emittance, and luminescence. These characteristic absorption and emittance bands occur in narrow wavelength ranges, 10 mm or less, frequently much less; and, unless the instruments have that kind of spectral resolution, these details cannot be recorded. Many laboratory and field instruments have the needed spectral resolution, but only recently has such capability entered the domain of airborne and space remote sensing. Basically, remote sensing is the task of counting and recording photons in terms of intensity versus wavelength, phase, and polarization--photons associated with reflectance, emittance, and luminescence.
The recording of the reflected component of radiation is the most common form of remote sensing, and can involve the sun, lasers, and radio frequencies. The sun is the most frequently used source, and in the reflected solar spectrum (0.4-2.5 micra), supports sensors such as cameras, the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) bands 1, 2, 3, 4, 5, and 7 (band 6 being a thermal infrared band), SPOT, and the hyperspectral systems such as the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). The tone differences that define boundaries of shapes, soil changes, and the highlight and shadow tones are due to different amounts of radiation reflected to the sensor from the various surfaces.
The amount of radiation reflected from a surface depends on the wavelength band being used, its angle of incidence with the surface, the orientation of the sensor in relation to the surface and the illuminant, the material's molecular composition, and the surface structure. Molecular composition, however, sets the stage, and as we progress to remote sensors that record reflected energy in narrower and narrower bands, we become more interested in the details of the interaction of electromagnetic energy with matter.
A surface that reflects uniformly across the visible and photographic parts of the EM spectrum, is called a neutral surface--i.e., it does not treat any one wavelength different from any other. For normal angles of illumination no neutral material is a perfect reflector, i.e., 100% reflectance throughout the spectrum. Nor is it a perfect absorber, i.e., 0% reflectance. If it has a high reflectance, it is called white. If it has a low reflectance, it is called black. In between it is called gray.
Most materials reflect more photons of some wavelengths than of other wavelengths, e.g., a blue surface reflects blue light, and absorbs green and red, a yellow surface absorbs blue, and reflects green and red, whereas a green surface reflects green light, but absorbs blue and red. If three surfaces, e.g., blue, green, and red, reflect the same amount of photons--i.e., the intensity from the blue surface is the same as the green, which is the same as the red--then a black and white photograph would show them as the same gray tone--they would be indistinguishable. A normal color film, however, like the eye, is a multiband system. It has three bands, or channels, that separately document the intensities in the blue, green, and red bands, and displays the results as a color composite--showing that there are three different objects in the scene.
In the visible part of the spectrum, healthy vegetation absorbs blue light, reflects some of the photons in the green band, and absorbs photons in the red band--thus, we see healthy leafy vegetal material as green, and it would be recorded as such in a normal color film. Healthy leafy vegetation is much more reflective in the infrared part of the spectrum, than in the visible; which is why, in black and white infrared photography, most vegetation shows as very bright tones. Vegetation stress may be detected at the 1.4 and 1.9 micra regions which reflect water absorption. As a plant loses water, these bands become shallower, and thereby provide another indicator of vegetation stress.
The infrared brightness of healthy vegetation is a useful characteristic for military targeting. In WW II, Eastman Kodak was asked to develop a film that could separate green painted camouflaged targets from the green surrounds of vegetal material. It turns out that, though a visual match, green paint is much less reflective in the infrared, than is vegetation. At that time, color films, such as Kodachrome, were sensitive primarily to photons in the visible part of the spectrum. By extending the sensitivity of one of the three emulsion layers in Ektachrome film out to about 900 nm in the infrared region, Eastman Kodak developed the needed film, which was known as Camouflage Detection film, or CD film. With the proper filter, this film would portray green paint as green, and healthy vegetation as red. The emulsion that had the extended sensitivity was the one sensitive to bluelight, and which was coupled to the cyan dye. Increasing its sensitivity to infrared, out to 900 nm, greatly increased its sensitivity to the more energetic photons at double the frequency, or half the wavelength, i.e., 400-450 nm.
In fact, the emulsion was about ten times more sensitive to blue light than to infrared. Thus, in order to tell which photons, blue or infrared, were responsible for the red tone, a yellow filter was required to prevent blue light from entering the emulsion. In addition to infrared, such a filter passes two thirds of the visible spectra, i.e., green and red, which causes the visual sensation we call yellow. Because the filter rejects the blue third, it is also called a minus-blue filter. Examples include Wratten 11, 12, and 13. With the blue light eliminated, the red response could be attributed to the vegetation--and any green painted object stuck out in stark contrast as green. Because the colors in an infrared photograph are not colors that eyes see in the scene, such a photograph is also called a false color image.
One response of stressed vegetation is loss of the bright infrared reflectance. The stress can be induced by drought, flooding, chemical sprays, senescence, by biological infections such as the rust and wilt, or by infestations such as gypsy moths. In all instances the loss of infrared reflectance provides the basis for detecting the presence of the stress, mapping its extent, and monitoring an area for change. On the military side, camouflage was, and still is, frequently made by cutting branches of trees to lay over the site--and this is effective, but only for a relatively short time. One response of the cut branch is that the leafy material loses its infrared reflectance faster than it loses its green reflectance. Even though looking equally green to the eye, the CD film shows the reflectance loss of the damaged vegetation as dark tones of red/green and green.
After WW II, CD film was evaluated for applications in geology, forestry, wetlands, land use mapping, and for detecting stressed vegetation. It became so useful that the consumer market constantly increased. Eastman Kodak improved the film, and changed its name to Ektachrome Infrared film (EIR).
Normal color film has three spectral bands, or recording channels, i.e., blue, green, and red, which are analogous to bands 1, 2 and 3 of the Landsat Thematic Mapper. The color combines the three channels into a normal color image--a color composite. Adding infrared sensitivity makes four channels--blue, green, red, and infrared-analogous to Landsat TM bands 1, 2, 3 and 4. Taking the photograph through a yellow filter, eliminates channel 1, the blue band, and the final colors in the image result from mixes of intensities in the green, red, and infrared bands. With Landsat TM, the selection is made electronically. Of the seven bands available, one can select band 2, 3 and 4, couple them to blue, green, and red guns and create a false color composite image that is similar to the image from the EIR film with a yellow filter.
In general, the reflectance of earth materials increases as one goes to longer wavelengths. In the ultraviolet, most surfaces have a similar low reflectance. This is why photographs taken in ultraviolet, and in blue light tend to be flat--there is little contrast. Reflectance steadily increases going from blue, to green, to red, and to infrared. It is in the infrared region that brightness, contrast, and other interesting spectral features begin to develop. The reflectance characteristics of playa surface materials, such as calcite, halite, montmorillonite, kaolinite and gypsum have common absorption bands. Strong water absorption bands at 1400 nm and 1900 nm are apparent for some of the materials. For atmospheric water vapor, these broad bands do not represent a single absorption, but a collection of narrower bands. In the 1400 nm region, free water has several absorption bands, including ones at 1350 nm, 1380 nm, and 1460 nm. Unless the spectrometer has a narrow spectral bandpass, these closely adjacent bands cannot be resolved--they blend into one larger band. The 1400 nm band for gypsum shows structure within it that suggest the presence of other bands. A record that shows both bands, i.e., 1400 nm and 1900 nm, indicates undissociated water such as water of hydration, or water trapped in the lattice. Molecular water, which is an important component in gypsum, has other absorption bands known as overtones and combinatorial tones.
Although the molecular make-up of a material establishes the absorption characteristics, the reflected component can be greatly affected by other factors, an important one being surface structure. Surface structure can be defined in terms of wavelength of the radiation concerned. For a surface to be considered a high quality reflector, or mirror, it must be flat to within about a quarter of a wavelength of the radiation to be used. Under this condition, the bulk of the incoming radiation is reflected at an angle that equals the angle of incidence. This is called specular reflection. If the topographic variations of the surface exceed this, then proportionally more of the energy is scattered in other directions, i.e., the diffuse component gets smaller. For remote sensors using reflected sunlight, the radiation wavelengths fall between 400 nm and 2500 nm, or between 0.4 and 2.5 micra. Aside from water, most surfaces have relief variations in excess of this, and are diffuse scatters, e.g., leaves, bark, soil, rocks, concrete, etc. Consequently, when viewing terrain from some point in space, the image tones can vary, even be reversed, as a function of viewing angle in relation to the sun. Looking in the up-sun direction, the sensor receives both the diffuse and specular reflection components. Down-sun, the sensor receives diffuse minus specular, i.e., only the backscatter. If the surface variations have an orderly structure, the image patterns are influenced by sun azimuth as well. For example, the ridges and furrows of a freshly plowed field have relief on the order of 15 to 20 cm. Furthermore, it is an ordered relief, i.e., a pattern of parallel lines. If the SuII's rays are parallel to the ridge pattern, the surface receive about the same amount of illumination and the image of the field would have an overall uniform tone. If the sun's rays are perpendicular to the ridge pattern, the field shows as a parallel series of highlights and shadows. X- and C-band radars would show a similar display. For P-band radar (100 cm wavelength), however, the plowed field would be a speculum, or mirror, and would be a dark area, an area of no return, or radar loss, from any angle of illumination.
Radiation absorbed by a material leads to other effects. For one, absorbed photons increase the internal energy, or temperature, of the material, which, in turn, increases the quantity and alters the wavelength distribution of the thermally emitted radiation, both infrared and microwave. This is the most common outcome of absorption, and is the basis for thermal, or passive, remote sensing in either the infrared or the microwave domain. During daylight hours, absorbed sunlight heats the terrain. During the night the terrain cools by radiating into space, and would continue to cool except that the sun comes up and renews the heating cycle.
Thermal infrared techniques are associated with two wavelength regions for which the atmosphere is transparent, i.e., atmospheric windows. These are the 3.5-5.5 and 8-14 micra bands. Although thermal techniques are considered by many to be outside the hyperspectral domain, we include them for practical reasons. First, characteristics of thermal images and the events that influence them, are so different from images formed with reflected radiation that they deserve special mention. Second, terrain analysts in DoD, must be able to provide information from any imagery source. Third, thermal systems provide a different type of image, which is important for targeting. Fourth, these systems are available, and being used. Examples include the Landsat TM band 6 (10.4-12.5 micra wavelength), and the Thermal Infrared Mapping system (TIMS), which has six bands in the 8-14 micra range.
At temperature above absolute zero (0.degree. K. or 273.degree. C.), all matter emits electromagnetic energy. If hot enough, such as a hot stovepipe, a tungsten light bulb, or the sun, the object emits enough energy to effect the eye, or photographic emulsion. At normal earth surface temperatures (-50.degree. to +50.degree. C.), however, the amount of energy emitted is below the threshold level of either a photographic emulsion, or the eye. To detect such low levels of photons, special materials are not only sensitive to infrared radiation, but which have some property, such as electrical resistance, that changes rapidly and significantly with variations in intensity of the incoming radiation. This signal is amplified, displayed on a cathode ray tube and recorded on magnetic tape, or on photographic emulsions via a modulated glow tube, or other device. By convention, the images are printed so that light tones represent warmer surfaces, and dark tones represent cooler surfaces.
The amount of energy emitted from a surface, and its wavelength distribution, depend on temperature. The amount is equal to the fourth power of the absolute temperature multiplied by the emissivity. For a given temperature, the wavelength distribution curve will have a maximum intensity as a specific wavelength known as lambda sub-max (.lambda.max). As the temperature gets higher, .lambda.max shifts to shorter wavelength, e.g., at -150.degree. C. it is about 23.2 micra, at 0.degree. C. it is about 10.5 micra, and at 100.degree. C. it is about 7.7 micra. At the sun's temperature, .lambda.max is about 0.5 micra. Also, as the temperature gets higher, the short wavelength edge of the distribution curve includes higher energy levels, i.e., it goes to shorter wavelengths, and the area under the curve (total energy emitted) gets rapidly larger. Keeping emissivity constant, a warmer body will emit more energy at all wavelengths than will a cooler body, and will incorporate a short wavelength increment denied the cooler material. For normal earth surface temperatures, i.e., -50.degree. C. to 50.degree. C., the wavelength of peak emission is in the 8-14 micra band, which is one of the atmospheric windows. About 40% of the energy is emitted in this band, and about 3% in the 3.0-5.5 micra band. For mapping thermal variations in the terrain the 8-14 micra band is the preferred choice.
Above 250.degree. C., the wavelength of peak emission enters the 3.0-5.5 micra band. For detecting hot targets, this band is the preferred choice. The signal/noise ratio, or the target/background contrast is much greater here. Emissivity, which is wavelength dependent, denotes how good an absorber, or emitter, a material is. Molecules emit energy only at those wavelengths they can absorb. A perfect absorber, or emitter, has an emissivity of 1. A material that absorbs 50% of the incoming radiation, and reflects 50%, has an emissivity of 0.5. So, if two materials have the same physical temperature, but differ in their emissivities, the one with the higher emissivity will emit more energy than the one with the lower, and will be brighter in the image. Many earth materials have emissivities in the 0.7-0.9 range, which means they are fairly good absorbers. This being so, radiation emanating from them is pretty much of surface and near-surface origin. Radiation from molecules at depth is absorbed by molecules above, radiated and absorbed by the next layer, and passed along until there are molecules that can radiate into space. For infrared, the depth of the layer that radiates into space is a fraction of a millimeter. The effective depth of this layer increases with longer wavelengths, being perhaps 2.5 cm in the microwave L-band (23 cm).
This layer is an interface between the material below, and the atmosphere above, and is readily influenced by events on both sides. Below, energy transfer is associated with conduction and, in some cases, diffusion. Above, atmospheric variables take over. These can quickly, and radically, alter the radiation characteristics of the surfaces--eliminating, subduing, or increasing thermal contrast. Thus, thermal contrast is influenced by a variety of diurnal and seasonal variations in climatic and meteorological factors, such as wind, atmospheric pressure, dew, rain, humidity, incoming space radiation, etc. Wind can override subsurface conductive events and imprint its own temperature regime, which can create confusing thermal patterns in the form of wind shadows--i.e., surfaces in the lee can be much warmer, or cooler, than surfaces exposed to the wind.
Objects sticking up into the air take on the temperature characteristics of the air. At nighttime, when the air is warmer than the ground, which is cooling by radiation loss, these objects will appear as hotspots. As the night progresses, the air layer, cooled by contact with the cooler ground, becomes thicker, and sequentially cools taller objects that project up into it. By late night the hotspots disappear, except for the tallest trees, or for natural or artificially maintained heat sources.
Also, cooler air is more dense, and being more dense, it flows downslope to settle in the lows, which show as darker tones in the thermal imagery. Darker tones in the lows can also be caused by moist soil, and can lose heat faster. As a result. cool air drainage into the lows is sometimes mistaken for moist soils.
For detection of voids in the terrain, such as caves, tunnels, crevasses in an icecap, or buried installations, atmospheric pressure changes are critical. First, the interior temperatures are usually fairly constant, and are warmer or cooler than the ground surfaces at some time of the day. Also, infrared cannot penetrate the overburden to reveal the presence of the void. The best time to fly is when the void is exhaling, and the outpouring flow of warm air through various openings brings the temperature of the surrounds to above ambient, and they become detectable--i.e., one detects the openings, not the void. Such an outpouring can occur only when the atmospheric pressure is less than the air pressure in the void. Thus, the time to fly is on a descending pressure front.
A second effect of photon absorption is luminescence. There are materials that can absorb photons of one frequency and emit photons of a lower frequency, i.e., lower energy, without any significant increase in temperature. These materials are said to be luminescent. An example is the emission of visible light from minerals when they are illuminated with "black" light, or ultraviolet radiation. Luminescence is an emission of radiation due to electronic transitions, and there are two kinds--fluorescence, which occurs from an excited single state, and phosphorescence, which results from an excited triple state. A distinction can also be made on the basis of time--i.e., how long does the light last after the excitation energy is turned off? This is called the decay time. In fluorescence, the decay time is very short, ranging from 10.sup.-9 to 10.sup.-3 seconds. For example, the fluorescence decay time of rhodamine B in water is about 2.5 nanoseconds (ns). In phosphorescence, the decay time is longer--sometimes much longer. Calcium sulfide, for example, can continue to glow for several hours after the excitation illumination is turned off.
Luminescent techniques require an energy source to excite, or raise the electrons to higher energy levels, and darkness in order to detect the luminesced photons. The sun meets both of these needs--as does the nighttime use of lasers. The sun does not emit a continuous spectrum, i.e., energy at all wavelengths, or frequencies. Although such is generated in the hot core, electronic absorption by elements and ionized atoms in the cooler envelope greatly reduces the intensities of many of the frequencies. When the sun is examined with a good spectroscope, one finds that there are gaps--many gaps--wherein energy is greatly reduced, or absent. These gaps of darkness are narrow in bandwidth, so narrow they are called lines--specifically, Fraunhofer Lines in honor of their discoverer. The spectral bandwidth of these lines are measured in Angstrom units. The ultraviolet, visible, and near infrared portion of the solar spectrum contain over 30,000 Fraunhofer Lines, or lines of darkness. These lines provide the darkness needed for detection of luminescence, and the sun's radiation provides the excitation energy on the short wavelength side of the lines.
A sensor system that can look at both the sun and the earth's surface with detectors sensitive to energy in these dark lines can detect the presence of luminescence photons from the earth's surface, or target. If, from the target, it detects a certain intensity in a dark line band, it cannot be reflected solar energy because such is not coming from the sun. The fill-in must, therefore, be due to luminesced photons from the target. Such is the function of the Fraunhofer Line Discriminator (FLD).
Such things as temperature and pH can alter the characteristics of the luminesced photons. Some materials that are under constant, or steady-state illumination, give a different luminescence signal after an hour or two, than they do to an instant measurement immediately after excitation. These changes show as an increase in intensity at longer wavelengths of emission and a decrease of the shorter wavelength components. In fact, some molecules show little luminescence when first illuminated, but develop an intense emission after steady-state illumination. These characteristics are usually associated with liquids and are indicative of changes caused by chemical reactions. Also, the recorded emission spectrum can be distorted in the short wavelength region by self-absorption within the solution. Whether or not these factors are of concern to remote sensing of earth surface and targeting materials is moot. Some materials, such as vegetation, have a near surface liquid component, and chemical reactions are taking place--e.g., photosynthesis. Furthermore, these surfaces are receiving steady state illumination from the sun for hours. In laboratory measurements, the illumination. i.e., the excitation mode, is of short duration. Another characteristic of luminescence is that for any specific wavelength of excitation, there is an emission spectra that can take place over a fairly broad wavelength band, and the decay times of the longer wavelengths can be considerably longer than those of the shorter wavelengths. The total is still a very short time. In laboratory experiments, decay time spectra have shown links to material types and conditions. Whether or not such has application in remote sensing remains to be determined.
Because all materials reflect, absorb, or emit photons in ways characteristic of their molecular makeup, a high resolution trace of the intensity of the transmitted, reflected, emitted, or luminesced radiation versus wavelength forms a graphical record unique to a given material. Different materials cannot have identical spectral wave shapes of reflectance, emittance, and luminescence. Many of the characteristic absorption and emission bands occur in narrow wavelength ranges, 10 nm or less; and, unless the instruments have that kind of spectral resolution, these details cannot be recorded. Although many laboratory and field instruments exceed this spectral resolution, airborne systems have only recently entered this domain. From a laboratory point of view, the use of spectral measurements to identify and/or assay components of minerals, pigments, pharmaceutical and other organic and inorganic compounds, is old, established, and reliable. With reference to remote sensing, the reasoning goes that if such could be done from air or space, it would give remote sensing a similar capability.
Photographic emulsions were the earliest of the sensors to document landscape scenes, and human activities. By the late 1800's emulsions went airborne, via balloons and kites, to replace the observer and his notepad for recording terrain characteristics and military items of interest. By WW I, cameras were in airplanes and routinely involved in reconnaissance and targeting. It was recognized early on that if one could sample different wavelength of radiation, and compare them, one would have a better chance ol detecting targets, as well as noting changes in the landscape--a multiband concept, although not called that, was now in place.
The first steps were taken in the late 1940's and the 1950's when the Army, along other groups, divided the photographic portion of the electromagnetic spectrum, 400 to 900 nm, into narrower bandpasses by means of various combinations of photographic emulsions and filters. The goal was to improve techniques for detecting targets and mapping conditions such as camouflage, vegetation type, vegetation stress, soil moisture, flood damage, wetland boundaries, to name a few, and the term multiband photography came into being to describe these efforts. Camouflage Detection film, and its improvement into Ektachrome Infrared film is one example of a successful film/filter combination, or multiband approach which later passed into the digital domain of Landsat as the False Color Composite. The bandpasses were still broad, however, ranging from 60 to 100 nm. Nevertheless, multiband photography had applications to some forms of targeting, and change detection.
Next came the Landsat MSS, which recorded reflected sunlight in four broad bands--two in the visible, each of which is 100 nm wide, and two in the infrared, with one being 100 nm wide and the other 1.1 micra. This was followed by the Landsat TM with six bands in the reflected solar region, and one band in the thermal infrared, with the narrowest band being band 3 at 60 nm. Whatever spectral variations occur in the terrain within any of these bands are average out to arrive at a digital number (DN) representing the brightness for the whole band. Extensions of the multispectral concept into the thermal infrared region of the spectrum include the Advanced Very FIigh Resolution Radiometer (AVHRR), and the airborne TIMS developed by Daedalus enterprises, Inc.
In the early 1980's, a system came forth that greatly altered the existing concepts of multispectral remote sensing with reflected solar energy. This was the Airborne Imaging Spectrometer (AIS) developed by the Jet propulsion Laboratory (JPL). The AIS records reflected solar energy in some 128 channels, or images, within the 1.2-2.4 micra region of the spectrum and with a spectral bandwidth for each channel of less than 10 nm. The AIS evolved into the AVIRIS with some 220 raw data channels, or images, within the 0.4-2.45 micra portion of the spectrum. Resanipling gives 210 spectral bands of radiometrically calibrated data. The instantaneous field of view (IFOV) is 1 milliradian, or about 10 meters at operational altitude. Each image is a record of the intensity of reflected sunlight within a spectral bandwidth of less than 10 nm. After calibrations and corrections have been made, the intensity values of the 210 channels, for any given picture element (pixel), can be called up and sequentially displayed along the wavelength axis, as a spectrophotometric trace, i.e., radiometric intensity versus wavelength. Because of the narrowness of the bands, as well as their multiplicity, these systems are called hyperspectral, to differentiate them from the broad band systems, e.g., MSS, TM, SPOT, etc. Systems are also being developed that can operate in even narrower bands, i.e., the sub-nanometer range, for working with gaseous emissions and absorptions. These are called ultraspectral systems.
As indicated, hyperspectral refers to a multiplicity of recording channels that have relatively narrow bandwidths. Since the advent of the AIS and the AVIRIS, other airborne narrow bandpass systems have been developed, and plans laid for satellite follow-ons. The later include the Shuttle Imaging Spectrophotometer Experiment (SISEX), and the High Resolution Imaging Spectrometer (HIRIS). Details of these systems can be found in a Proceedings Issue of the Society of Photo-Optical instrumentation Engineers (Vane, 1987b).
Because the atmosphere absorbs many wavelength components of the incoming sunlight, as well as of the reflected energy en route to the sensor, corrections are needed for many targets. If one is interested in vegetation, this involved the depth and shapes of water absorption bands. Because water vapor is a component of the atmosphere, the analyst does not know how much of the depth and shape of those water bands is due to atmospheric absorption, and how much is due to vegetation absorption. If corrections can be made to remove the atmospheric component via available models such as LowTran, then the residuum can be attributed to plant water.
The notion has been expressed that this is overload, and that such a multiplicity of bands will lead to data constipation in the collection system, the transmission system, and the data reduction and manipulation systems; and, to ease this, unneeded bands should be eliminated from the collection system. If one thinks of hyperspectral imagery as an extension of Landsat, and plans to use the techniques of band rationing throughout 220 channels--then, as far as the data reduction and data manipulation systems go, constipation is at hand. The important point is that, although such band rationing can be done, one can go to a direct call-up of the spectral reflectance plot for any selected area. Nevertheless, reducing the number of channels is thought to be desirable by a number of agencies. Which ones can be eliminated--which ones are unneeded? If you have a narrowly defined goal, the question is easier to answer. For targeting minerals, the geologist can get by with perhaps 30 to 40 bands. For determining crop quantity and quality, the agriculturist can get by with perhaps 20 bands, only a portion of which overlap the geologists' needs. The army, with its interest in terrain, targeting, and intelligence, has need for information about identities, and properties associated with vegetation, soils, rocks, minerals, and cultural objects including camouflage. Perhaps reduction can be made--perhaps there are bands that have no use for anybody--but, it is too early for declarations.
There is another important benefit to an imaging spectrometer. It provides two domains of information for evaluation-image patterns and spectral patterns. From the standpoint of terrain information in terms of materials identities and conditions, potential for dust generation, location of engineering materials, engineering site selection and evaluation, probable location of ground water., surface waste disposal, etc., the manual analysis of stereo imagery is still state-of-the-art. For example, an area can be covered with a vegetative mantle of grass and trees, and all that the spectral data will show will be reflectance traces of chlorophyll. In stereoscopic viewing, however, the shapes of the landform and drainage can reveal that beneath the vegetal mantle rests a thinly interbedded series of limestones and shales dipping gently to the west, and with unstable colluvial materials on the lower slopes.
At present, imaging spectrometers provide only monoscopic imagery, so there is a reduction in the quantity and quality of information that can be derived on the basis of image pattern shapes; but these shapes are present, and they can make significant direct contributions to an analysis, as well as assist in the valuation of the spectral data. Furthermore, existing routines for combining bands to make color composite images, such as Landsat, or the Coastal Zone color Scanner (CZCS), can be directly applied to hyperspectral data.
In any event, the airborne imaging spectrometers are here, the spaceborne systems are in development, and the hyperspectral concept is sound. The issues to be resolved include: what are these systems suited for?--what are their advantages, disadvantages, and limitations?--and, how well will they work?
Spectral data from imaging spectrometers can be evaluated on the basis of: shape of the overall curve, or portions of it; intensity differences at any selected wavelength range; wavelength location of absorption bands; and, depth and shape of absorption bands. To link these to identities and conditions requires an extensive computer library of field and laboratory measurements of spectral reflectance, luminescence, and emittance throughout the reflected solar, and thermal infrared portions of the spectrum--and the software to make the evaluations and comparisons. TEC has a spectral reflectance/luminescence data base of over 1,000 samples of soils, rocks, vegetation, and man-made materials. Such a library needs excellent documentation, because these measured values change with a variety of factors for any given surface, the molecular makeup determines the basic characteristics of absorption, reflectance luminescence, and emittance. These in turn, are modified by structure of the surface, and its orientation in relation to the sensor and to the illuminating source. For example, maintaining a constant field of view and a constant viewing angle, while measuring spectral reflectance at different sun angles and elevations, can result in variances of plus or minus 10 percent. With reference to structure, vegetation can have smooth, crenelated, or wrinkled leaf surfaces, and the leaves and stems can have many different sizes and be arranged in many different ways. This means different highlight/shadow ratio, different amounts of transmitted and re-reflected infrared energy through the biomass, and different amounts of radiation reflecting up through the vegetation from the soil surface.
For a given mineral composition, the spectral signature of a fine textures soil can differ from that of a coarser textured soil. Then, there are the influences of conditions--a term used to denote such things as age, growth phase, wet, dry, weathered, lichen covered, etc. New leaves have a different spectral signature than older leaves, wet soil is different than the same soil when dry, a wheathered rock surface differs from a fresh surface. In reality, these are different chemical forms, which gets back to the earlier statement that the molecular makeup of a surface establishes the basis of reflectance and absorptance. Keeping the target surface and illumination/sensor angles constant, the spectral signature is further modified by climate, season, and meteorological variations. Changes in incoming short and long wave radiation from space, wind and atmospheric pressure greatly alter radiometric signatures, as well as target/background contrasts in thermal imagery.
Multiplicity of measurements is necessary because there can be significant variation within any given class of targets, especially in field measurements. For example, one can measure 20 creosote bushes that look alike and are about the same size and age. But, the result will likely be 20 slightly different spectra-perhaps plus or minus 10% variance, or more, from a derived norm. The variations are mostly in intensity, not wavelength locations of absorption bands. Although the plants look alike, they are not identical--each has some variance in biomass, structure, openness, etc. these factors alter the characteristics of the energy reflected from the vegetal surfaces, as well as the characteristics of the contributing reflected soil component passing through, or reflected from the canopy.
For current systems and typical target areas, the IFOV (10 meters for AVIRIS) encompasses a mixture of surfaces, and the resulting spectral signature is a composite of individual signatures--which presents another problem in relation to digital analysis of spectra data.
In few cases principal component analysis was employed to reduce data ollected by remote sensing systems. See, for example, C. Bradue, N. Ben Yosef and I. Dor "Satellite remote sensing of waste water reservoirs" Int. J. Remote Sensing, 1995, Vol. 16, No. 16, pp. 3087-3114; and A. Picchiotti, R. Casacchia and R. Salvatori "Multitemporal principal component analysis of spectral and spatial features in the Venice lagoon", Int. J. Remote Sensing, 1997, Vol. 18, No. 1, pp. 183-196, both are incorporated by reference as if fully set forth herein. However, the prior art does not teach the application of a decorrelation statistical analysis to full spectra. In the first reference cited above the remote sensing system employed is the SPOT which measures three wide bands. In the second reference cited above employed are only six spectral bands and the principal component analysis was projected over time and not over spectral data.
The present invention is directed at providing a system (hardware and software) which performs a measurement, with higher sensitivity and at higher speed, and encompassing a much smaller amount of data from the outset. The hardware does not require an interferometer, but only a number (N) of what is herein referred to as "decorrelation matched filters", which are placed in the path of the incoming light beam from the remote scenes to be measured. The filters may be of a fixed nature or tunable (AOTF or LCTF). In the latter case a single tunable filter is used to sequentially mimic the decorrelation matched filters under electronic control.